Inter-species comparisons of gene expression levels will increase our understanding of the evolution of transcriptional mechanisms and help to identify targets of natural selection. This approach holds particular promise for apes, as many human-specific adaptations are thought to result from differences in gene expression rather than in coding sequence. Expression differences have also been associated with phenotypes of medical importance, including numerous diseases as well as differential drug response. To identify genes whose regulation is likely to be of functional importance in humans, we propose to compare gene expression levels in liver and kidney within and between five humans, five chimpanzees, five orangutans, and five rhesus macaques. To do so, we will develop and use multi-species cDNA arrays that enable the measurement of genes expression differences between species, while accounting for the effect of sequence divergence on hybridization intensity. We will focus on two sets of genes: first, those whose expression levels are approximately constant across individuals from all four species, and whose regulation is therefore likely to be under stabilizing selection. This set will represent promising candidates for disease-association studies. Using a new population genetic approach that we developed, we will use polymorphism and divergence data from these genes to infer the strength and mode of natural selection acting on their upstream regulatory regions. We will also identify genes whose expression levels are constant in the three non-human primates, but consistently elevated or reduced in humans. These regulatory changes are likely to underlie human-specific adaptations. Finally, to enable this type of study for any tissue, we will design and optimize genome-wide multi-species oligonucleotide arrays. In summary, the proposed research will lead to the identification of the first set of genes whose expression regulation is likely to evolve under natural selection in humans and will shed light onthe relationship between gene expression patterns and the evolution of cis-regulatory regions.